CBR快速检索算法在时间序列预测中的应用

随着CBR应用的推广,涉及越来越多的时态信息需要处理.探讨了一种基于时间序列数据的时态CBR,提出了一种基于卷积的时态CBR快速检索算法.其思路是利用时序范例之间的时间约束关系,去除检索中求取相似度的冗余计算,并利用卷积的傅立叶变换性质,在频域求解相似度以减少计算时间复杂度.实验证明.在匹配较长的序列时,快速算法可以显著的提高时态CBR的检索效率.在CBR快速检索算法的基础上,以证券价格预测问题作为应用,借鉴流形学习理论中LLE算法的思想,设计了一种基于时态CBR的时间序列预测算法.实验证明,这种基于时态CBR的时间序列预测方法与前述CBR快速检索算法相配合,取得了较好的预测效果和预测效率.     

基于卷积的时态CBR快速检索算法及应用*

探讨了如何增强CBR对一种常见的时态信息,即时间序列数据的检索能力;分析了已有的基于傅里叶频谱分析的时间序列检索算法应用于CBR时遇到的问题,并根据时态CBR检索的需要,提出了一种新的基于循环卷积和傅里叶变换时间序列检索算法.理论分析和数值实验结果都证明,提出的算法在检索效率上有一定的优势.将采取这种检索方法的时态CBR应用于时间序列的预测问题中,取得了较好的预测效果且具有较高的预测效率.     

用遗传模拟退火算法挖掘特征项权重的研究

能否在范例库中检索和选择出最为相似的范例决定了范例推理系统性能。文中介绍了遗传算法和模拟退火算法，比较了两种算法的特性．提出一种混合遗传模拟退火算法。该算法不但具有强的局部搜索能力．还缩短了搜索时间。将该算法用于发掘范例库上特征权重，理论分析和实验结果表明了这种混合遗传模拟退火算法优于普通的遗传算法。     

一种时间序列案例的综合相似度函数

针对案例推理中案例检索的相似度函数这个关键问题,本文分析了时间序列案例的结构特点,提出了一种时间序列案例的相似度函数.讨论了常用相似度函数的不足,提出了一种综合相似度函数.最后,提出了一种时间序列案例的综合相似度函数,并在气象数据上进行了对比检验与分析.结果表明.该函数不仅可行,而且比常用函数更优.     

基于范例库推理的软件成本估算模型研究

用传统的经验函数估算软件成本有很多局限性，采用基于范例库推理的估算方法可以很好地弥补其中的问题。讨论了软件成本估算和基于CBR推理研究的特点，从总体上阐述了COSCBR系统结构，描述了系统重要的研究方面：影响软件成本因素；层次推理；COSCRB系统的范例表示方法；相似度的基本计算算法。     

偏序约简框架下增量自学习推理：税务稽查的例子

本文讨论欺诈防范领域中税务稽查的例子。在相关文献基础上分析了目前线性推理的不足，提出构造偏序约简范例集，给出了CBR循环过程中范例获取、记忆、扩容、推理等算法，由此实现范例推理机增量自学习机制。算法相比线性检索和记忆有着较高的性能和准确度，在税务稽核选案、信用卡欺诈、公司财务数据审计方面都可以有相当广阔的应用。     

一个基于范例推理的时序预测模型

本文在相似模型的统一描述的基础上，提出一个多层次的抽象范例重用框架，适用于进行描连和时序的预测。在时间序列的问题下，本文描述了多层次范例推理的方法，并且讨论了一些CBR循环常见的问题在时序预测中的情况。本文最后提供一个期货预测的例子，对本文的模型作了说明。     

范例推理技术在银行客户信用评估中的应用

范例推理技术是人工智能领域中一种基于知识的问题求解和学习方法。为了有效评估银行客户信用等级并提高银行信贷业务效率，文中提出了范例推理技术(CBR)在银行客户信用评估中的应用，并给出了基于范例推理的银行客户信用评估系统的原型，介绍了该系统中的关键技术：范例表示、相似性计算和范例检索，研究了归纳学习、特征子集选择等机器学习方法在范例检索中的应用。     

基于CBR和XML的软构件检索方法

在对现有构件检索方法分析的基础上，探讨了一种基于案例推理和XML技术的智能化软件构件的检索框架。重点阐述了构件案例库中构件的XML知识表示方法以及构件检索中需求构件和案例库中构件的相似度评估方法，提出了一种计算案例相似度的递归算法。     

时间序列相似性半监督谱聚类

时间序列相似度是时间序列数据挖掘的重要研究方向之一。如何利用时间序列相似度对提高时间序列数据聚类有着重要的意义。提出一种基于时间序列相似度的半监督谱聚类算法,通过选取适当的时间序列特征构造相似度与距离,在谱聚类算法的基础上利用标签数据选取初始类簇。实验表明,该算法使具有相似特征的时间序列可以很有效地被聚集到同一类中。     

Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

Abductive case‐based reasoning

This article introduces abductive case‐based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.     

基于CBR与灰色关联度的财务危机预警

提出一种基于案例推理(CBR)与灰色关联度的企业财务危机预警模型。将灰色关联分析应用于企业财务危机预警的案例推理中,采用特征属性的主客观权重计算案例相似度。根据各特征属性对案例检索的重要程度,通过权重向量排除非关键指标对案例判断的干扰。实验结果表明,该方法得到的案例相似性排序结果符合实际情况,可提高相似企业的检索效率,满足企业财务危机预警的要求。     

CBR系统中一种实例相似度算法的改进

本文对CBR系统中实例相似度的算法进行了改进,在传统实例相似性算法的基础上加入实例属性缺失度因子和实例可复用性因子。因此在实例检索中考虑实例检索结果的精确度和实例的可复用度,进而使检索的精确度提高和修改难度降低,最终提高CBR系统的精确度和实用性。     

P2P case storage and retrieval with an unspecified ontology

Traditional approaches for similarity-based retrieval of structured data, such as Case-Based Reasoning (CBR), have been largely implemented using centralized storage systems. In such systems, when the cases contain both numeric and free-text attributes, similarity-based retrieval cannot exploit standard speedup techniques based on multi-dimensional indexing, and the retrieval is implemented by an exhaustive comparison of the case to be solved with the whole set of stored cases. In this work, we review current research on Peer-to-Peer (P2P) and distributed CBR techniques and propose a novel approach for storage of the case-base in a decentralized Peer-to-Peer environment using the notion of Unspecified Ontology to improve the performance of the case retrieval stage and build CBR systems that can scale up to large case-bases. We develop an algorithm for efficient retrieval of approximated most-similar cases, which exploits inherent characteristics of the unspecified ontology in order to improve the performance of the case retrieval stage in the CBR problem solving cycle. The experiments show that the algorithm successfully retrieves cases close to the most-similar cases, while reducing the number of cases to be compared. Hence, it improves the performance of the retrieval stage. Moreover, the distributed nature of our approach eliminates the computational bottleneck and single point of failure of the centralized storage systems.     

基于偏好信息的案例检索算法

案例推理方法建立在“相似问题具有相似解”的基础上,能否从案例库中检索出与新问题“最相似”的案例是案例推理方法成功的关键因素之一。该文提出一种改进的检索方法,在原始最近相邻算法基础上,用专家对新问题案例与历史案例属性差异的效用评价替代原始的属性差异值来衡量专家对属性差异的敏感程度。引入变异系数来标度新问题案例与历史案例的属性差异的分布情况,从而保证检索出的最相似案例具有较高的属性差异的均衡性。通过具体案例检索实例分析,验证了该方法的有效性。     

Combining feature reduction and case selection in building CBR classifiers

CBR systems that are built for the classification problems are called CBR classifiers. This paper presents a novel and fast approach to building efficient and competent CBR classifiers that combines both feature reduction (FR) and case selection (CS). It has three central contributions: 1) it develops a fast rough-set method based on relative attribute dependency among features to compute the approximate reduct, 2) it constructs and compares different case selection methods based on the similarity measure and the concepts of case coverage and case reachability, and 3) CBR classifiers built using a combination of the FR and CS processes can reduce the training burden as well as the need to acquire domain knowledge. The overall experimental results demonstrating on four real-life data sets show that the combined FR and CS method can preserve, and may also improve, the solution accuracy while at the same time substantially reducing the storage space. The case retrieval time is also greatly reduced because the use of CBR classifier contains a smaller amount of cases with fewer features. The developed FR and CS combination method is also compared with the kernel PCA and SVMs techniques. Their storage requirement, classification accuracy, and classification speed are presented and discussed.